L07 Layers

Data Visualization (STAT 302)

Overview

The goal of this lab is to explore more plots in ggplot2 and continue to leverage the use of various layers to build complex and well annotated plots.

Datasets

We’ll be using the tech_stocks.rda dataset which is already in the /data subdirectory in our data_vis_labs project.

We have a new dataset, NU_admission_data.csv, which will need to be downloaded and added to our /data subdirectory.

We will also be using the mpg dataset which comes packaged with ggplot2 — use ?ggplot2::mpg to access its codebook.

Code

# load package(s)
library(tidyverse)
library(dplyr)
library(janitor)
library(lubridate)
library(patchwork)
# load datasets
nu_data = read_csv("data/NU_admission_data.csv") %>% 
 janitor::clean_names()
load("data/tech_stocks.rda")
mgp = ggplot2::mpg %>% 
  janitor::clean_names()

Exercise 1

Using mpg and the class_dat dataset created below, recreate the following graphic as precisely as possible in two different ways.

Hints:

• Transparency is 0.6
• Horizontal spread is 0.1
• Larger points are 5
• Larger points are “red”

Code

# additional dataset for plot
class_dat <- mpg %>%
  group_by(class) %>%
  summarise(
    n = n(),
    mean_hwy = mean(hwy),
    label = str_c("n = ", n, sep = "")
  )

Plot 1 – using mean_hwy

Code

ggplot(mpg, aes(class, hwy)) + 
  geom_jitter(width = 0.1)+
  geom_point(data = class_dat,
             aes(class, mean_hwy),
             size = 5,
             color = "red",
             alpha = 0.6)+
  geom_text(data = class_dat,
            mapping = aes(label = label,y = 10),
            vjust = "inward")+
  theme_minimal()+
  labs(
      x = "Vehicle Class",
      y = "Highway miles per gallon"
  )

Plot 2 – not using mean_hwy

Code

ggplot(mpg, aes(class, hwy)) + 
  geom_jitter(width = 0.1)+
  geom_point(stat = "summary", 
             fun = "mean", 
             colour = "red", 
             size = 5, 
             alpha = 0.6)+
  geom_text(data = class_dat,
            mapping = aes(label = label, y = 10),
            vjust = "inward")+
  theme_minimal()+
  labs(
      x = "Vehicle Class",
      y = "Highway miles per gallon"
  )

Exercise 2

Using the perc_increase dataset derived from the tech_stocks dataset, recreate the following graphic as precisely as possible.

Hints:

• Hex color code #56B4E9
• Justification of 1.1
• Size is 5

Code

# percentage increase data
perc_increase <- tech_stocks %>%
  arrange(desc(date)) %>%
  distinct(company, .keep_all = TRUE) %>%
  mutate(
    perc = 100 * (price - index_price) / index_price,
    label = str_c(round(perc), "%", sep = ""),
    company = fct_reorder(factor(company), perc)
  )

Code

ggplot(perc_increase, aes(x = perc, y = company)) + 
  geom_col(
           aes(y = company), 
           fill = "#56B4E9"
           )+
  geom_text(
            mapping = aes(label = label),
            hjust = 1.1,
            color = "white",
            size = 5
            )+
  scale_x_continuous(
    expand = c(0, 0)
  )+
  labs(
    x = NULL,
    y = NULL
  )+
  theme_minimal()+
  theme(
    axis.ticks = element_blank()
  )

Exercise 3

Warning — see video

Some thoughtful data wrangling will be needed and it will be demonstrated in class — Do not expect a video.

Using NU_admission_data.csv create two separate plots derived from the single plot depicted in undergraduate-admissions-statistics.pdf — this visual and data has been collected from https://www.adminplan.northwestern.edu/ir/data-book/. They overlaid two plots on one another by using dual y-axes.

Create two separate plots that display the same information instead of trying to put it all in one single plot — stack them using patchwork or cowplot.

There is one major error they make with the bars in their graphic. Explain what it is.

Answer:Something wrong with the bars that the bar and the data are not matched.

Which approach do you find communicates the information better, their single dual y-axes plot or the two separate plot approach? Why?

Answer: I think they both have advantages. Although putting two graphs together can summary more information with limited spaces, I like putting them in separate plots. Because if I just want to focus on the admission rate versus yield rate, my judgement won’t be bothered by the bar plot.

Hints:

• Form 4 datasets (helps you get organized, but not entirely necessary):
  • 1 that has bar chart data,
  • 1 that has bar chart label data,
  • 1 that has line chart data, and
  • 1 that has line chart labels
• Consider using ggsave() to save the image with a fixed size so you it is easier to pick font sizes.

Code

# bar plot data
bar_data = nu_data %>% 
  select(-contains("_rate")) %>% 
  pivot_longer(
    cols = -year,
    names_to = "category",
    values_to = "count"
  )
#bar label data
bar_label_data = bar_data %>% 
  mutate(
    col_label = prettyNum(count, big.mark = ",")
  )
# build plot
# bar_plot

(nu_bar_plot = ggplot(data = bar_data, aes(x = year, y = count))+
  geom_col(aes(fill = category), 
           position = "identity",
           width = 0.7)+
  geom_text(
    data = bar_label_data,
    mapping = aes(label = col_label),
    size = 1.5,
    color = "white",
    vjust = 1,
    nudge_y = -200
  )+
  scale_x_continuous(name = "Entering Year",
                     breaks = 1999:2020,
                     expand = c(0,0.25))+
  scale_y_continuous(name = "Applications",
                     expand = c(0,0),
                     limits = c(0,50000),
                     labels = scales::label_comma(),
                     breaks = seq(0, 50000, 5000))+
  scale_fill_manual(
    name = NULL,
    limits = c("applications", "admitted_students", "matriculants"),
    labels = c("Applications", "Admitted Students", "Matriculants"),
    values = c("#B6ACD1", "#836EAA", "#4E2A84")
  )+
  theme_classic()+
  theme(
    legend.justification = c(0.5,1),
    legend.position = c(0.5,1),
    legend.direction = "horizontal",
    plot.title = element_text(hjust = 0.5)
  )+
  ggtitle("Northwestern University\nUndergraduate Admissions 1999-2020")
)

Code

#ggsave("nu_bar_plot.pdf", bar_plot, width = 9, height = 4)

Code

## bulid line chart
rate_data = nu_data %>% 
  select(year, contains("_rate")) %>% 
  pivot_longer(
    cols = -year,
    names_to = "category",
    values_to = "value"
  ) %>% 
  mutate(
    rate_labels = paste0(value, "%"),
    label_y = case_when(
      category == "admission_rate" ~ value - 2,
      category == "yield_rate" ~ value + 2
    )
  )

#bulid plot
(nu_rate_plot = ggplot(data = rate_data, 
                       aes(x = year, y = value))+
  geom_line(aes(color = category))+
  geom_point(
    mapping = aes(shape = category, fill = category),
    color = "white")+
  geom_text(
    mapping = aes(y = label_y, label = rate_labels),
    size = 2
  )+
  scale_x_continuous(name = "Entering Year",
                     breaks = 1999:2020,
                     expand = c(0, 0.35))+
  scale_y_continuous(name = NULL,
                     expand = c(0, 0),
                     limits = c(0, 70),
                     labels = scales::label_percent(scale = 1),
                     breaks = seq(0, 70, 10))+
  scale_color_discrete(
    name = NULL,
    labels = c("Admission Rate", "Yield Rate")
  )+
  scale_shape_manual(
    labels = c("Admission Rate", "Yield Rate"),
    name = NULL,
    values = c(21, 24)
  )+
  scale_fill_discrete(name = NULL,
                      labels = c("Admission Rate", "Yield Rate"))+
  theme_classic()+
  theme(
    legend.justification = c(0.5,1),
    legend.position = c(0.5,1),
    legend.direction = "horizontal",
    plot.title = element_text(hjust = 0.5)
  )+
  ggtitle("Northwestern University\nUndergraduate Admissions 1999-2020")
)

Code

# graphic
nu_rate_plot / (nu_bar_plot + ggtitle(NULL))